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Understanding Differential Data Privacy

Collecting and analyzing aggregated data is essential in today’s connected world, but are you doing everything necessary to ensure the privacy of the individuals behind that data? Not only is it ethical and moral to take necessary security precautions when dealing with consumer data, but it’s also a legal requirement to remain compliant and avoid hefty fines, major PR nightmares, and damage to your brand image.

One increasingly effective and popular way to avoid getting into hot water by applying the principle of differential privacy. Differential privacy isn’t so much a specific privacy technique, like de-identification, but rather a property that a data point or process possesses that determines whether that data can be considered private.

To put it another way, differential privacy is the mathematical definition of the extent to which data must be protected, altered, or anonymized before it’s private enough to avoid re-identification with the individual connected to it.

Differential privacy is designed to remove the guesswork from balancing data privacy and data utility. It’s a mathematically defined way of making data useful for analysis and sharing, and complying with GDPR, CCPA, and other data privacy regulations – without revealing any sensitive information.

How Does Differential Data Privacy Work?

Differential privacy involves adding randomness to a data set in proportion to the potential privacy threat posed by that data set. The more identifying information a data set contains, and the more easily it could be traced back to the individuals to which it applies, the more randomization will be automatically applied.

The concept of differential privacy is based on the idea that for any given data set, there is an exact mathematical amount of randomness that must be applied to keep the data both private and useful, and that specific amount, called ε, varies based on the data itself. The value of ε for a given data set represents the ‘ideal’ amount of randomness that will balance utility and privacy.

What are the Benefits of Differential Data Privacy?

Differential data privacy is becoming more widespread thanks to some distinct advantages over previous approaches to data privacy.

Assumes Identifying Information

Asking a human (or in some cases even a machine) to determine the difference between identifying information and non-identifying information can be difficult or even impossible. With differential privacy, all information is assumed to have identifying potential, and the randomness value is assigned based on this assumption.

Less Exposure to Privacy Attacks

Even de-identified data can be linked back to the individuals it describes through linkage attacks. But differential data privacy is more resistant to attacks that are based on auxiliary information, making it a powerful tool in keeping data private.

Compositional Security

One way that de-identified data can potentially lead to loss of privacy is through the aggregated information gleaned from multiple individual analyses. If two distinct analyses are applied to the same data, the combined data gathered can provide enough information to link data to its sources.

However, differential data privacy is compositional, meaning it’s possible to determine the exact level of privacy loss of running two private analyses. With that information, steps can be taken to reasonably assure privacy.

Automatically Adjusts Query Result Accuracy

Differential privacy will automatically adjust the accuracy of a given query based on the properties of the query itself. This helps ensure that the results you get are as accurate as possible, while still retaining an acceptable level of privacy. The less threat to privacy a query introduces, the more accurate and detailed the results can be.

Customized Levels of Privacy

The custom level of privacy provided by differential data means that there’s no one-size-fits-all approach. This avoids risking overexposure of some data to leaks or obscuring data too much for useful analysis.

Challenges of Differential Privacy

Differential privacy isn’t flawless, even when implemented correctly. For one, it isn’t applicable to the entire range of potential cybersecurity problems.

Individual level analysis is no longer possible when differential privacy has been applied to a data point. That means an analyst is blocked from learning information about a specific individual — great for protecting against hackers, but not so great when it comes to making data useful for personalized analysis.

Another potential complication is that even experts can’t quite agree on the optimal level of distortion that must be applied to the data to retain privacy and utility. However, as differential privacy and its applications become more widespread across organizations and industries, there will likely be a consensus about the best value for ε — at least as it relates to a series of use cases.

Differential Data Privacy Use Cases

Speaking of use cases — here are some of the most recent and newsworthy ways that differential data privacy has been in the public eye:

  • Google introduced a differential privacy tool of its own in 2014, called Randomized Aggregatable Privacy-Preserving Ordinal Response, or RAPPOR. It’s a Chrome browser feature that allows Google to analyze and pull important insights from the usage of its flagship browser without allowing tracing or unauthorized access of sensitive information. In 2019, Google went a step further by releasing open source differential privacy libraries.
  • iOS and macOS devices designed and released by Apple use differential data privacy, allowing for the security and analysis of personal data ranging from search queries and health information to which emojis are used most often.
  • The U.S. Census Bureau incorporated differential privacy for the first time in the 2020 Census, allowing it to collect detailed information about U.S. citizens’ demographics without allowing that information to be traced.

Ready to Implement Differential Privacy?

Immuta is a cybersecurity and data access control platform built to empower data teams with everything from automated data, security, access control, and privacy protection — including differential privacy.

In fact, Immuta currently holds a patent for a process of enhancing differential privacy in query results. We’ve designed our platform in such a way that enables effective differential privacy to be implemented more easily than ever. For our customers, data can be both useful and secure — keeping their customers and data protected. We let you automate every step of the way to make differential privacy simple, easy, and seamless alongside your everyday processes. See how it’s done in this short video.

 

Ready to learn more? Try Immuta today.